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Guest Editors' Introduction: Frontiers of Hardware and Algorithms for On-chip Learning.

, , , and . JETC, 14 (2): 14:1-14:2 (2018)

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Measuring scheduling efficiency of RNNs for NLP applications., , , and . CoRR, (2019)Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications., , and . CoRR, (2019)Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks., , , , , , , and . FPGA, page 16-25. ACM, (2016)Compressing RNNs for IoT devices by 15-38x using Kronecker Products., , , , , , and . CoRR, (2019)BONSEYES: Platform for Open Development of Systems of Artificial Intelligence: Invited paper., , , , , , , , , and 5 other author(s). Conf. Computing Frontiers, page 299-304. ACM, (2017)Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs., , , , , and . CoRR, (2019)Run-Time Efficient RNN Compression for Inference on Edge Devices., , , , and . CoRR, (2019)Guest Editors' Introduction: Frontiers of Hardware and Algorithms for On-chip Learning., , , and . JETC, 14 (2): 14:1-14:2 (2018)Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction., , , , and . UbiComp, page 1016-1027. ACM, (2016)Skipping RNN State Updates without Retraining the Original Model., , , and . SenSys-ML, page 31-36. ACM, (2019)